docstr: Returns a dictionnary of all metrics and losses being tracked
          
docstr_tok: ["Returns","a","dictionnary","of","all","metrics","and","losses","being","tracked"]
code: def get_metrics(self, verbose=False):
      """Returns a dictionnary of all metrics and losses being tracked"""
      metrics = super().get_metrics(verbose)
      metrics["{}_Cmiou".format(self._stage)] = self._Cmiou * 100
      metrics["{}_Imiou".format(self._stage)] = self._Imiou * 100
      if self._full_res:
          metrics["{}_full_Cmiou".format(self._stage)] = (
              self._full_Cmiou * 100
          )
          metrics["{}_full_Imiou".format(self._stage)] = (
              self._full_Imiou * 100
          )
      if verbose:
          metrics[
              "{}_Imiou_per_class".format(self._stage)
          ] = self._miou_per_class
          if self._full_res:
              metrics[
                  "{}_full_Imiou_per_class".format(self._stage)
              ] = self._full_miou_per_class
      return metrics
  
file_path: torch-points3d/torch_points3d/metrics/shapenet_part_tracker.py
imports: import numpy
  from .confusion_matrix import ConfusionMatrix
  from .base_tracker import meter_value, BaseTracker
  from torch_geometric.data import Data
  from torch_points3d.models import model_interface
  from torch_points3d.core.data_transform import SaveOriginalPosId
  from torch_points3d.metrics.segmentation_helpers import SegmentationVoter
label_desc: Use image node_id 7 for calling the ShapenetPartTracker obj's underlying member method code with example usage: obj.get_metrics(verbose) and returns: metrics